Overview

Dataset statistics

Number of variables64
Number of observations2248299
Missing cells102014614
Missing cells (%)70.9%
Total size in memory1.1 GiB
Average record size in memory520.0 B

Variable types

Text10
Numeric49
Unsupported5

Alerts

START_WI weight has 1881704 (83.7%) missing valuesMissing
START_WI index has 1881704 (83.7%) missing valuesMissing
DO_WI weight has 1906411 (84.8%) missing valuesMissing
DO_WI index has 1906418 (84.8%) missing valuesMissing
PAX_WI weight has 1881704 (83.7%) missing valuesMissing
PAX_WI index has 1940506 (86.3%) missing valuesMissing
TOTAL_DEADLOAD_WI weight has 1965218 (87.4%) missing valuesMissing
TOTAL_DEADLOAD_WI index has 1965219 (87.4%) missing valuesMissing
TOTAL_LOAD_WI has 1942718 (86.4%) missing valuesMissing
TOTAL_TRAFFIC_LOAD has 1942718 (86.4%) missing valuesMissing
FUEL_INDEX has 1988785 (88.5%) missing valuesMissing
AZFW has 1906411 (84.8%) missing valuesMissing
ATOW has 1906411 (84.8%) missing valuesMissing
ALAW has 1906411 (84.8%) missing valuesMissing
ATXW has 1906411 (84.8%) missing valuesMissing
AFT_LIMIT_ZFW has 1919273 (85.4%) missing valuesMissing
FWD_LIMIT_ZFW has 1919411 (85.4%) missing valuesMissing
AFT_LIMIT_TOW has 1919297 (85.4%) missing valuesMissing
FWD_LIMIT_TOW has 1919297 (85.4%) missing valuesMissing
AFT_LIMIT_LAW has 2202341 (98.0%) missing valuesMissing
FWD_LIMIT_LAW has 2202075 (97.9%) missing valuesMissing
LIZFW has 1906436 (84.8%) missing valuesMissing
LITOW has 1906414 (84.8%) missing valuesMissing
LILAW has 1906421 (84.8%) missing valuesMissing
MAC_AT_ZFW has 1906499 (84.8%) missing valuesMissing
MAC_AT_TOW has 1906499 (84.8%) missing valuesMissing
MAC_AT_LAW has 1906499 (84.8%) missing valuesMissing
DEADLOAD_MAC has 1965219 (87.4%) missing valuesMissing
UNDERLOAD has 1965498 (87.4%) missing valuesMissing
LIMITING_WEIGHT has 2248299 (100.0%) missing valuesMissing
ALLOWED TOW has 1906411 (84.8%) missing valuesMissing
ALLOWED ZFW has 1906412 (84.8%) missing valuesMissing
ALLOWED LAW has 1906412 (84.8%) missing valuesMissing
ALLOWED TXW has 1906411 (84.8%) missing valuesMissing
STABTO has 2164914 (96.3%) missing valuesMissing
OPTIMAL_TRIM has 1919480 (85.4%) missing valuesMissing
IDEAL_ADDITIONAL_LOAD_AFT has 2032810 (90.4%) missing valuesMissing
IDEAL_ADDITIONAL_LOAD_FWD has 2144362 (95.4%) missing valuesMissing
TAIL_TIPPING_WI weight has 1906411 (84.8%) missing valuesMissing
TAIL_TIPPING_WI index has 1906411 (84.8%) missing valuesMissing
TAIL_TIPPING_INDEX_EXCEEDED has 2248299 (100.0%) missing valuesMissing
FWD_MOVABLE_PAX has 2248299 (100.0%) missing valuesMissing
AFT_MOVABLE_PAX has 2248299 (100.0%) missing valuesMissing
INDEX_OUT_OF_BALANCE has 2225151 (99.0%) missing valuesMissing
LOAD_TO_AFT has 2241481 (99.7%) missing valuesMissing
LOAD_TO_FWD has 2247720 (> 99.9%) missing valuesMissing
ESTIMATED_TRAFFIC_LOAD has 1942718 (86.4%) missing valuesMissing
ESTIMATED_ZFW has 1906411 (84.8%) missing valuesMissing
DELTA_ZFW has 2061504 (91.7%) missing valuesMissing
ZFW_TOLERANCE_EXCEEDED has 2248299 (100.0%) missing valuesMissing
Total bag weight has 1996812 (88.8%) missing valuesMissing
continent has 171222 (7.6%) missing valuesMissing
START_WI weight is highly skewed (γ1 = 41.96893947)Skewed
DO_WI weight is highly skewed (γ1 = 20.39234211)Skewed
DO_WI index is highly skewed (γ1 = 26.34591483)Skewed
PAX_WI weight is highly skewed (γ1 = 24.86631473)Skewed
TOTAL_DEADLOAD_WI index is highly skewed (γ1 = 26.98097857)Skewed
TOTAL_LOAD_WI is highly skewed (γ1 = 42.65303523)Skewed
TOTAL_TRAFFIC_LOAD is highly skewed (γ1 = 25.68234569)Skewed
AZFW is highly skewed (γ1 = 21.75572416)Skewed
ALAW is highly skewed (γ1 = 20.22151083)Skewed
ATXW is highly skewed (γ1 = 27.11258989)Skewed
FWD_LIMIT_ZFW is highly skewed (γ1 = 38.68887444)Skewed
AFT_LIMIT_TOW is highly skewed (γ1 = 25.58406376)Skewed
LIZFW is highly skewed (γ1 = 24.54428541)Skewed
LITOW is highly skewed (γ1 = 24.85882749)Skewed
LILAW is highly skewed (γ1 = 30.19903386)Skewed
ALLOWED TOW is highly skewed (γ1 = 45.18755629)Skewed
ALLOWED ZFW is highly skewed (γ1 = 531.6440864)Skewed
ALLOWED LAW is highly skewed (γ1 = 483.6973484)Skewed
ALLOWED TXW is highly skewed (γ1 = 431.2761542)Skewed
OPTIMAL_TRIM is highly skewed (γ1 = 28.22627342)Skewed
IDEAL_ADDITIONAL_LOAD_AFT is highly skewed (γ1 = 35.33252022)Skewed
IDEAL_ADDITIONAL_LOAD_FWD is highly skewed (γ1 = 76.03918959)Skewed
TAIL_TIPPING_WI weight is highly skewed (γ1 = 384.8702935)Skewed
ESTIMATED_TRAFFIC_LOAD is highly skewed (γ1 = 31.66180485)Skewed
ESTIMATED_ZFW is highly skewed (γ1 = 22.40642059)Skewed
DELTA_ZFW is highly skewed (γ1 = 146.3950883)Skewed
Total bag weight is highly skewed (γ1 = 83.16872844)Skewed
id has unique valuesUnique
LIMITING_WEIGHT is an unsupported type, check if it needs cleaning or further analysisUnsupported
TAIL_TIPPING_INDEX_EXCEEDED is an unsupported type, check if it needs cleaning or further analysisUnsupported
FWD_MOVABLE_PAX is an unsupported type, check if it needs cleaning or further analysisUnsupported
AFT_MOVABLE_PAX is an unsupported type, check if it needs cleaning or further analysisUnsupported
ZFW_TOLERANCE_EXCEEDED is an unsupported type, check if it needs cleaning or further analysisUnsupported
PAX_WI weight has 67158 (3.0%) zerosZeros
PAX_WI index has 83840 (3.7%) zerosZeros
TOTAL_LOAD_WI has 93569 (4.2%) zerosZeros
TOTAL_TRAFFIC_LOAD has 32775 (1.5%) zerosZeros
FUEL_INDEX has 130144 (5.8%) zerosZeros
ESTIMATED_TRAFFIC_LOAD has 32386 (1.4%) zerosZeros
DELTA_ZFW has 103275 (4.6%) zerosZeros

Reproduction

Analysis started2024-06-10 14:48:41.035550
Analysis finished2024-06-10 14:49:08.549615
Duration27.51 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct147029
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:09.404060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters42717681
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5248 ?
Unique (%)0.2%

Sample

1st row2024-04-30 04:01:00
2nd row2024-04-30 04:01:00
3rd row2024-04-30 04:01:00
4th row2024-04-30 04:01:00
5th row2024-04-30 04:01:00
ValueCountFrequency (%)
2024-05-03 351661
 
7.8%
2024-05-06 339404
 
7.5%
2024-05-01 337272
 
7.5%
2024-05-02 292312
 
6.5%
2024-05-04 289874
 
6.4%
2024-05-05 279874
 
6.2%
2024-04-30 238274
 
5.3%
2024-05-07 119628
 
2.7%
01:35:27 959
 
< 0.1%
01:35:28 934
 
< 0.1%
Other values (63347) 2246406
50.0%
2024-06-10T16:49:10.486312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9799291
22.9%
2 6112297
14.3%
- 4496598
10.5%
: 4496598
10.5%
4 3810711
 
8.9%
5 3467673
 
8.1%
1 2719386
 
6.4%
3 2509669
 
5.9%
2248299
 
5.3%
6 1113684
 
2.6%
Other values (3) 1943475
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42717681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9799291
22.9%
2 6112297
14.3%
- 4496598
10.5%
: 4496598
10.5%
4 3810711
 
8.9%
5 3467673
 
8.1%
1 2719386
 
6.4%
3 2509669
 
5.9%
2248299
 
5.3%
6 1113684
 
2.6%
Other values (3) 1943475
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42717681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9799291
22.9%
2 6112297
14.3%
- 4496598
10.5%
: 4496598
10.5%
4 3810711
 
8.9%
5 3467673
 
8.1%
1 2719386
 
6.4%
3 2509669
 
5.9%
2248299
 
5.3%
6 1113684
 
2.6%
Other values (3) 1943475
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42717681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9799291
22.9%
2 6112297
14.3%
- 4496598
10.5%
: 4496598
10.5%
4 3810711
 
8.9%
5 3467673
 
8.1%
1 2719386
 
6.4%
3 2509669
 
5.9%
2248299
 
5.3%
6 1113684
 
2.6%
Other values (3) 1943475
 
4.5%

id
Real number (ℝ)

UNIQUE 

Distinct2248299
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150627951.4
Minimum33764913
Maximum375567251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:10.818276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum33764913
5-th percentile33882137.9
Q134344940.5
median137944452
Q3138513928.5
95-th percentile375451178.1
Maximum375567251
Range341802338
Interquartile range (IQR)104168988

Descriptive statistics

Standard deviation118378389.4
Coefficient of variation (CV)0.7858992191
Kurtosis-0.2218045626
Mean150627951.4
Median Absolute Deviation (MAD)103463283
Skewness1.006157239
Sum3.386566726 × 1014
Variance1.401344308 × 1016
MonotonicityNot monotonic
2024-06-10T16:49:11.139566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33766922 1
 
< 0.1%
138324393 1
 
< 0.1%
138324411 1
 
< 0.1%
138324412 1
 
< 0.1%
138324413 1
 
< 0.1%
138324390 1
 
< 0.1%
138324391 1
 
< 0.1%
138324392 1
 
< 0.1%
138324427 1
 
< 0.1%
138324355 1
 
< 0.1%
Other values (2248289) 2248289
> 99.9%
ValueCountFrequency (%)
33764913 1
< 0.1%
33764914 1
< 0.1%
33764915 1
< 0.1%
33764916 1
< 0.1%
33764917 1
< 0.1%
ValueCountFrequency (%)
375567251 1
< 0.1%
375567250 1
< 0.1%
375567249 1
< 0.1%
375567248 1
< 0.1%
375567243 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:11.383209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4496598
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAB
2nd rowAB
3rd rowAB
4th rowAB
5th rowAB
ValueCountFrequency (%)
mn 1108949
49.3%
ab 709595
31.6%
zy 429755
 
19.1%
2024-06-10T16:49:11.849072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1108949
24.7%
N 1108949
24.7%
A 709595
15.8%
B 709595
15.8%
Z 429755
 
9.6%
Y 429755
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4496598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1108949
24.7%
N 1108949
24.7%
A 709595
15.8%
B 709595
15.8%
Z 429755
 
9.6%
Y 429755
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4496598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1108949
24.7%
N 1108949
24.7%
A 709595
15.8%
B 709595
15.8%
Z 429755
 
9.6%
Y 429755
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4496598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1108949
24.7%
N 1108949
24.7%
A 709595
15.8%
B 709595
15.8%
Z 429755
 
9.6%
Y 429755
 
9.6%

flight_number
Real number (ℝ)

Distinct2044
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2381.133745
Minimum0
Maximum9902
Zeros86
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:12.181180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1089
Q11276
median2102
Q32563
95-th percentile5605
Maximum9902
Range9902
Interquartile range (IQR)1287

Descriptive statistics

Standard deviation1498.802002
Coefficient of variation (CV)0.629448894
Kurtosis2.881307708
Mean2381.133745
Median Absolute Deviation (MAD)654
Skewness1.678547455
Sum5353500618
Variance2246407.442
MonotonicityNot monotonic
2024-06-10T16:49:12.651284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2608 12239
 
0.5%
1156 12067
 
0.5%
1158 11898
 
0.5%
2486 11891
 
0.5%
2485 11609
 
0.5%
2525 11564
 
0.5%
1152 11527
 
0.5%
2607 11272
 
0.5%
1122 11086
 
0.5%
1045 10772
 
0.5%
Other values (2034) 2132374
94.8%
ValueCountFrequency (%)
0 86
< 0.1%
1 47
< 0.1%
2 99
< 0.1%
3 64
< 0.1%
4 67
< 0.1%
ValueCountFrequency (%)
9902 143
 
< 0.1%
9900 126
 
< 0.1%
9889 2
 
< 0.1%
9888 532
< 0.1%
9787 66
 
< 0.1%

flight_date
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.198636391
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:13.012017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile30
Maximum30
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.46891592
Coefficient of variation (CV)1.176461132
Kurtosis2.799183857
Mean7.198636391
Median Absolute Deviation (MAD)2
Skewness2.054003705
Sum16184687
Variance71.72253686
MonotonicityNot monotonic
2024-06-10T16:49:13.381950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 308828
13.7%
2 292655
13.0%
4 277550
12.3%
6 272540
12.1%
1 272367
12.1%
5 269179
12.0%
30 226015
10.1%
7 156706
7.0%
10 21956
 
1.0%
9 21695
 
1.0%
Other values (17) 128808
5.7%
ValueCountFrequency (%)
1 272367
12.1%
2 292655
13.0%
3 308828
13.7%
4 277550
12.3%
5 269179
12.0%
ValueCountFrequency (%)
30 226015
10.1%
29 18657
 
0.8%
28 1604
 
0.1%
27 1322
 
0.1%
23 2
 
< 0.1%
Distinct184
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:14.499053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6744897
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIXB
2nd rowIXB
3rd rowIXB
4th rowIXB
5th rowIXB
ValueCountFrequency (%)
dub 839768
37.4%
blr 149219
 
6.6%
bom 146928
 
6.5%
vcp 84621
 
3.8%
del 79922
 
3.6%
gox 58495
 
2.6%
amd 47997
 
2.1%
pnq 47642
 
2.1%
lhr 45760
 
2.0%
rec 45706
 
2.0%
Other values (174) 702241
31.2%
2024-06-10T16:49:15.565698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 1271096
18.8%
D 1064112
15.8%
U 934334
13.9%
O 360028
 
5.3%
L 349235
 
5.2%
C 332087
 
4.9%
R 317051
 
4.7%
M 263301
 
3.9%
A 210355
 
3.1%
P 189930
 
2.8%
Other values (16) 1453368
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6744897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1271096
18.8%
D 1064112
15.8%
U 934334
13.9%
O 360028
 
5.3%
L 349235
 
5.2%
C 332087
 
4.9%
R 317051
 
4.7%
M 263301
 
3.9%
A 210355
 
3.1%
P 189930
 
2.8%
Other values (16) 1453368
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6744897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1271096
18.8%
D 1064112
15.8%
U 934334
13.9%
O 360028
 
5.3%
L 349235
 
5.2%
C 332087
 
4.9%
R 317051
 
4.7%
M 263301
 
3.9%
A 210355
 
3.1%
P 189930
 
2.8%
Other values (16) 1453368
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6744897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1271096
18.8%
D 1064112
15.8%
U 934334
13.9%
O 360028
 
5.3%
L 349235
 
5.2%
C 332087
 
4.9%
R 317051
 
4.7%
M 263301
 
3.9%
A 210355
 
3.1%
P 189930
 
2.8%
Other values (16) 1453368
21.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:15.868622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.06738027
Min length5

Characters and Unicode

Total characters24882780
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowservice-acco
2nd rowservice-acco
3rd rowservice-acco
4th rowservice-acco
5th rowservice-acco
ValueCountFrequency (%)
service-acco 1948755
86.7%
human 299544
 
13.3%
2024-06-10T16:49:16.430392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 5846265
23.5%
e 3897510
15.7%
a 2248299
 
9.0%
s 1948755
 
7.8%
r 1948755
 
7.8%
v 1948755
 
7.8%
i 1948755
 
7.8%
- 1948755
 
7.8%
o 1948755
 
7.8%
h 299544
 
1.2%
Other values (3) 898632
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24882780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 5846265
23.5%
e 3897510
15.7%
a 2248299
 
9.0%
s 1948755
 
7.8%
r 1948755
 
7.8%
v 1948755
 
7.8%
i 1948755
 
7.8%
- 1948755
 
7.8%
o 1948755
 
7.8%
h 299544
 
1.2%
Other values (3) 898632
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24882780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 5846265
23.5%
e 3897510
15.7%
a 2248299
 
9.0%
s 1948755
 
7.8%
r 1948755
 
7.8%
v 1948755
 
7.8%
i 1948755
 
7.8%
- 1948755
 
7.8%
o 1948755
 
7.8%
h 299544
 
1.2%
Other values (3) 898632
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24882780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 5846265
23.5%
e 3897510
15.7%
a 2248299
 
9.0%
s 1948755
 
7.8%
r 1948755
 
7.8%
v 1948755
 
7.8%
i 1948755
 
7.8%
- 1948755
 
7.8%
o 1948755
 
7.8%
h 299544
 
1.2%
Other values (3) 898632
 
3.6%
Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:16.768169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length33
Mean length22.78049672
Min length14

Characters and Unicode

Total characters51217368
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheckinMsgProcessor
2nd rowStorePaxDataAction
3rd rowCalculateWeightAndTrimAction
4th rowCalculateWeightAndTrimAction
5th rowStorePaxDataAction
ValueCountFrequency (%)
calculateweightandtrimaction 744772
33.1%
storepaxdataaction 390862
17.4%
checkinmsgprocessor 264896
 
11.8%
transfercheckindataaction 85186
 
3.8%
crewmsgprocessor 69552
 
3.1%
assignlccaction 68460
 
3.0%
asmmsgprocessor 64024
 
2.8%
updateloadtableaction 63062
 
2.8%
createzfwmessageaction 50677
 
2.3%
updatecrewdataaction 50660
 
2.3%
Other values (57) 396148
17.6%
2024-06-10T16:49:17.449319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 5078887
 
9.9%
i 4192550
 
8.2%
a 4044604
 
7.9%
e 3840899
 
7.5%
o 3513920
 
6.9%
n 3444530
 
6.7%
c 3435095
 
6.7%
A 2839410
 
5.5%
r 2717823
 
5.3%
s 1957418
 
3.8%
Other values (37) 16152232
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51217368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 5078887
 
9.9%
i 4192550
 
8.2%
a 4044604
 
7.9%
e 3840899
 
7.5%
o 3513920
 
6.9%
n 3444530
 
6.7%
c 3435095
 
6.7%
A 2839410
 
5.5%
r 2717823
 
5.3%
s 1957418
 
3.8%
Other values (37) 16152232
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51217368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 5078887
 
9.9%
i 4192550
 
8.2%
a 4044604
 
7.9%
e 3840899
 
7.5%
o 3513920
 
6.9%
n 3444530
 
6.7%
c 3435095
 
6.7%
A 2839410
 
5.5%
r 2717823
 
5.3%
s 1957418
 
3.8%
Other values (37) 16152232
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51217368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 5078887
 
9.9%
i 4192550
 
8.2%
a 4044604
 
7.9%
e 3840899
 
7.5%
o 3513920
 
6.9%
n 3444530
 
6.7%
c 3435095
 
6.7%
A 2839410
 
5.5%
r 2717823
 
5.3%
s 1957418
 
3.8%
Other values (37) 16152232
31.5%

START_WI weight
Real number (ℝ)

MISSING  SKEWED 

Distinct201
Distinct (%)0.1%
Missing1881704
Missing (%)83.7%
Infinite0
Infinite (%)0.0%
Mean62069.14871
Minimum12388
Maximum12310606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:17.766760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12388
5-th percentile29185
Q141889
median44106
Q345024
95-th percentile123769
Maximum12310606
Range12298218
Interquartile range (IQR)3135

Descriptive statistics

Standard deviation270678.6292
Coefficient of variation (CV)4.360920599
Kurtosis1885.268414
Mean62069.14871
Median Absolute Deviation (MAD)2217
Skewness41.96893947
Sum2.275423957 × 1010
Variance7.326692028 × 1010
MonotonicityNot monotonic
2024-06-10T16:49:18.100254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41889 73668
 
3.3%
43414 8363
 
0.4%
43538 6982
 
0.3%
43591 6831
 
0.3%
52301 6632
 
0.3%
43963 6413
 
0.3%
43984 5969
 
0.3%
44332 5890
 
0.3%
43512 5783
 
0.3%
44537 5746
 
0.3%
Other values (191) 234318
 
10.4%
(Missing) 1881704
83.7%
ValueCountFrequency (%)
12388 8
 
< 0.1%
12405 31
 
< 0.1%
12572 20
 
< 0.1%
12577 18
 
< 0.1%
13191 497
< 0.1%
ValueCountFrequency (%)
12310606 165
< 0.1%
1124409 405
< 0.1%
1123998 205
< 0.1%
1123868 1
 
< 0.1%
1123606 206
< 0.1%

START_WI index
Real number (ℝ)

MISSING 

Distinct185
Distinct (%)0.1%
Missing1881704
Missing (%)83.7%
Infinite0
Infinite (%)0.0%
Mean44.92493414
Minimum4.65
Maximum83.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:18.594890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.65
5-th percentile34.9
Q137.43
median42.12
Q351.5
95-th percentile64.59
Maximum83.02
Range78.37
Interquartile range (IQR)14.07

Descriptive statistics

Standard deviation8.896043929
Coefficient of variation (CV)0.1980201886
Kurtosis0.1062579048
Mean44.92493414
Median Absolute Deviation (MAD)7.38
Skewness0.6503192911
Sum16469256.23
Variance79.13959758
MonotonicityNot monotonic
2024-06-10T16:49:18.915806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.5 63513
 
2.8%
52 12765
 
0.6%
38.1 9390
 
0.4%
50 8363
 
0.4%
33.8 7043
 
0.3%
50.9 6831
 
0.3%
36.43 6413
 
0.3%
35.76 5969
 
0.3%
37.87 5890
 
0.3%
37.95 5746
 
0.3%
Other values (175) 234672
 
10.4%
(Missing) 1881704
83.7%
ValueCountFrequency (%)
4.65 70
< 0.1%
5.19 79
< 0.1%
21.23 49
 
< 0.1%
21.26 143
< 0.1%
21.59 129
< 0.1%
ValueCountFrequency (%)
83.02 1
 
< 0.1%
70.6 6
 
< 0.1%
69.36 40
 
< 0.1%
69.19 751
< 0.1%
68.79 766
< 0.1%

DO_WI weight
Real number (ℝ)

MISSING  SKEWED 

Distinct676
Distinct (%)0.2%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean84538.28326
Minimum12648
Maximum12831060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:19.252675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12648
5-th percentile29847
Q143294
median44757
Q345523
95-th percentile127615
Maximum12831060
Range12818412
Interquartile range (IQR)2229

Descriptive statistics

Standard deviation609194.2382
Coefficient of variation (CV)7.206134483
Kurtosis416.1990277
Mean84538.28326
Median Absolute Deviation (MAD)1388
Skewness20.39234211
Sum2.890262459 × 1010
Variance3.711176198 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:20.103615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43294 60355
 
2.7%
44377 5344
 
0.2%
44617 5105
 
0.2%
44794 4799
 
0.2%
44741 4656
 
0.2%
43369 4565
 
0.2%
43379 4279
 
0.2%
44881 4278
 
0.2%
44951 4214
 
0.2%
54026 4191
 
0.2%
Other values (666) 240102
 
10.7%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
12648 8
 
< 0.1%
12665 19
< 0.1%
12745 1
 
< 0.1%
12825 11
< 0.1%
12832 20
< 0.1%
ValueCountFrequency (%)
12831060 66
 
< 0.1%
12821060 20
 
< 0.1%
12751030 136
< 0.1%
12741030 71
 
< 0.1%
12710355 326
< 0.1%

DO_WI index
Real number (ℝ)

MISSING  SKEWED 

Distinct676
Distinct (%)0.2%
Missing1906418
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean46.39720155
Minimum0.95
Maximum1053.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:20.752542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile35.14
Q137.42
median41.22
Q353.04
95-th percentile63.81
Maximum1053.15
Range1052.2
Interquartile range (IQR)15.62

Descriptive statistics

Standard deviation35.1472579
Coefficient of variation (CV)0.7575296943
Kurtosis739.1197836
Mean46.39720155
Median Absolute Deviation (MAD)6.28
Skewness26.34591483
Sum15862321.66
Variance1235.329738
MonotonicityNot monotonic
2024-06-10T16:49:21.217074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.04 53119
 
2.4%
36.61 5399
 
0.2%
50 5119
 
0.2%
50.9 4791
 
0.2%
52 4656
 
0.2%
53.81 4036
 
0.2%
39.4 3969
 
0.2%
37.28 3721
 
0.2%
36.81 3630
 
0.2%
52.15 3427
 
0.2%
Other values (666) 250014
 
11.1%
(Missing) 1906418
84.8%
ValueCountFrequency (%)
0.95 63
< 0.1%
1.49 79
< 0.1%
21.53 19
 
< 0.1%
22.56 9
 
< 0.1%
22.59 1
 
< 0.1%
ValueCountFrequency (%)
1053.15 38
 
< 0.1%
1035.14 158
< 0.1%
1034.28 91
< 0.1%
1034.24 42
 
< 0.1%
1034.23 74
< 0.1%

PAX_WI weight
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct4432
Distinct (%)1.2%
Missing1881704
Missing (%)83.7%
Infinite0
Infinite (%)0.0%
Mean11990.13913
Minimum0
Maximum2010615
Zeros67158
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:21.628178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11393
median11368
Q313002
95-th percentile19347
Maximum2010615
Range2010615
Interquartile range (IQR)11609

Descriptive statistics

Standard deviation63047.76523
Coefficient of variation (CV)5.258301388
Kurtosis650.5124651
Mean11990.13913
Median Absolute Deviation (MAD)2360
Skewness24.86631473
Sum4395525054
Variance3975020700
MonotonicityNot monotonic
2024-06-10T16:49:22.112509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67158
 
3.0%
78 9531
 
0.4%
156 4172
 
0.2%
312 1559
 
0.1%
234 1556
 
0.1%
390 990
 
< 0.1%
14175 917
 
< 0.1%
13050 842
 
< 0.1%
5460 673
 
< 0.1%
13745 567
 
< 0.1%
Other values (4422) 278630
 
12.4%
(Missing) 1881704
83.7%
ValueCountFrequency (%)
0 67158
3.0%
35 6
 
< 0.1%
75 1
 
< 0.1%
78 9531
 
0.4%
88 145
 
< 0.1%
ValueCountFrequency (%)
2010615 92
< 0.1%
1891060 5
 
< 0.1%
1871053 26
 
< 0.1%
1781068 69
< 0.1%
1571069 3
 
< 0.1%

PAX_WI index
Real number (ℝ)

MISSING  ZEROS 

Distinct3102
Distinct (%)1.0%
Missing1940506
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean7.442870603
Minimum0
Maximum45.51
Zeros83840
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:22.526147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.8
Q312.08
95-th percentile22.74
Maximum45.51
Range45.51
Interquartile range (IQR)12.08

Descriptive statistics

Standard deviation7.86590804
Coefficient of variation (CV)1.056837941
Kurtosis0.8871398643
Mean7.442870603
Median Absolute Deviation (MAD)5.8
Skewness1.087851412
Sum2290863.471
Variance61.8725093
MonotonicityNot monotonic
2024-06-10T16:49:22.934315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83840
 
3.7%
0.48 1512
 
0.1%
0.9 1479
 
0.1%
0.11 1109
 
< 0.1%
0.05 1046
 
< 0.1%
12.19 779
 
< 0.1%
1.47 696
 
< 0.1%
0.63 636
 
< 0.1%
0.47 595
 
< 0.1%
0.06 488
 
< 0.1%
Other values (3092) 215613
 
9.6%
(Missing) 1940506
86.3%
ValueCountFrequency (%)
0 83840
3.7%
0.01 101
 
< 0.1%
0.02 39
 
< 0.1%
0.03 258
 
< 0.1%
0.04 364
 
< 0.1%
ValueCountFrequency (%)
45.51 1
 
< 0.1%
45.01 1
 
< 0.1%
44.51 1
 
< 0.1%
44.11 10
< 0.1%
44.1 6
< 0.1%

TOTAL_DEADLOAD_WI weight
Real number (ℝ)

MISSING 

Distinct9236
Distinct (%)3.3%
Missing1965218
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean105199.9606
Minimum41933
Maximum14010683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:23.364912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum41933
5-th percentile43294
Q144971
median46180
Q349369
95-th percentile133009
Maximum14010683
Range13968750
Interquartile range (IQR)4398

Descriptive statistics

Standard deviation730925.2739
Coefficient of variation (CV)6.947961482
Kurtosis291.8130473
Mean105199.9606
Median Absolute Deviation (MAD)1426
Skewness17.0287239
Sum2.978011004 × 1010
Variance5.34251756 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:23.769622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43294 14512
 
0.6%
45466 7104
 
0.3%
44923 5564
 
0.2%
127386 2769
 
0.1%
44380 2632
 
0.1%
44617 1411
 
0.1%
44741 1399
 
0.1%
44377 1344
 
0.1%
44794 1298
 
0.1%
43379 1211
 
0.1%
Other values (9226) 243837
 
10.8%
(Missing) 1965218
87.4%
ValueCountFrequency (%)
41933 67
 
< 0.1%
42089 5
 
< 0.1%
42174 1
 
< 0.1%
42259 245
< 0.1%
42389 1
 
< 0.1%
ValueCountFrequency (%)
14010683 47
< 0.1%
13831068 7
 
< 0.1%
13610346 1
 
< 0.1%
13410608 3
 
< 0.1%
13310615 3
 
< 0.1%

TOTAL_DEADLOAD_WI index
Real number (ℝ)

MISSING  SKEWED 

Distinct4544
Distinct (%)1.6%
Missing1965219
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean46.8344174
Minimum1.1
Maximum1069.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:24.172489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile32.89
Q138.75
median46.47
Q352.54
95-th percentile57.95
Maximum1069.89
Range1068.79
Interquartile range (IQR)13.79

Descriptive statistics

Standard deviation34.74518632
Coefficient of variation (CV)0.741872927
Kurtosis772.0141574
Mean46.8344174
Median Absolute Deviation (MAD)6.57
Skewness26.98097857
Sum13257886.88
Variance1207.227972
MonotonicityNot monotonic
2024-06-10T16:49:24.504201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.04 12722
 
0.6%
52.87 5302
 
0.2%
35.14 2737
 
0.1%
49.47 2656
 
0.1%
33.64 2261
 
0.1%
55.68 1683
 
0.1%
50 1537
 
0.1%
52 1456
 
0.1%
35.53 1397
 
0.1%
36.61 1361
 
0.1%
Other values (4534) 249968
 
11.1%
(Missing) 1965219
87.4%
ValueCountFrequency (%)
1.1 1
< 0.1%
1.49 1
< 0.1%
1.54 2
< 0.1%
1.58 1
< 0.1%
1.61 1
< 0.1%
ValueCountFrequency (%)
1069.89 27
< 0.1%
1069.67 3
 
< 0.1%
1069.52 9
 
< 0.1%
1068.38 7
 
< 0.1%
1068.35 1
 
< 0.1%

TOTAL_LOAD_WI
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct6006
Distinct (%)2.0%
Missing1942718
Missing (%)86.4%
Infinite0
Infinite (%)0.0%
Mean2039.411082
Minimum0
Maximum910442
Zeros93569
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:24.821997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1546
Q32341
95-th percentile5051
Maximum910442
Range910442
Interquartile range (IQR)2341

Descriptive statistics

Standard deviation10328.56183
Coefficient of variation (CV)5.064482547
Kurtosis2177.881747
Mean2039.411082
Median Absolute Deviation (MAD)1151
Skewness42.65303523
Sum623205278
Variance106679189.5
MonotonicityNot monotonic
2024-06-10T16:49:25.159133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93569
 
4.2%
2172 10737
 
0.5%
1629 9105
 
0.4%
1086 4706
 
0.2%
1304 1219
 
0.1%
1369 1017
 
< 0.1%
2436 1008
 
< 0.1%
3552 846
 
< 0.1%
1500 824
 
< 0.1%
543 748
 
< 0.1%
Other values (5996) 181802
 
8.1%
(Missing) 1942718
86.4%
ValueCountFrequency (%)
0 93569
4.2%
1 2
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
910442 2
 
< 0.1%
710687 3
 
< 0.1%
571060 30
< 0.1%
451060 1
 
< 0.1%
411053 3
 
< 0.1%

TOTAL_TRAFFIC_LOAD
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13446
Distinct (%)4.4%
Missing1942718
Missing (%)86.4%
Infinite0
Infinite (%)0.0%
Mean16036.34697
Minimum0
Maximum2710681
Zeros32775
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:25.470766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110888
median13427
Q315293
95-th percentile24823
Maximum2710681
Range2710681
Interquartile range (IQR)4405

Descriptive statistics

Standard deviation73227.87163
Coefficient of variation (CV)4.566368622
Kurtosis706.5190354
Mean16036.34697
Median Absolute Deviation (MAD)2123
Skewness25.68234569
Sum4900402944
Variance5362321184
MonotonicityNot monotonic
2024-06-10T16:49:25.872948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32775
 
1.5%
14175 353
 
< 0.1%
14115 275
 
< 0.1%
13575 272
 
< 0.1%
12810 256
 
< 0.1%
14055 254
 
< 0.1%
12950 253
 
< 0.1%
13960 246
 
< 0.1%
13050 237
 
< 0.1%
13910 228
 
< 0.1%
Other values (13436) 270432
 
12.0%
(Missing) 1942718
86.4%
ValueCountFrequency (%)
0 32775
1.5%
65 28
 
< 0.1%
75 1
 
< 0.1%
302 1
 
< 0.1%
592 1
 
< 0.1%
ValueCountFrequency (%)
2710681 21
< 0.1%
2601052 2
 
< 0.1%
2511053 18
< 0.1%
2411034 3
 
< 0.1%
2311069 1
 
< 0.1%

FUEL_INDEX
Real number (ℝ)

MISSING  ZEROS 

Distinct779
Distinct (%)0.3%
Missing1988785
Missing (%)88.5%
Infinite0
Infinite (%)0.0%
Mean2.730133326
Minimum0
Maximum26.5
Zeros130144
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:26.460452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.49
95-th percentile8.85
Maximum26.5
Range26.5
Interquartile range (IQR)5.49

Descriptive statistics

Standard deviation3.955331308
Coefficient of variation (CV)1.448768553
Kurtosis8.970317001
Mean2.730133326
Median Absolute Deviation (MAD)0
Skewness2.261875128
Sum708507.82
Variance15.64464575
MonotonicityNot monotonic
2024-06-10T16:49:26.854881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130144
 
5.8%
6 7615
 
0.3%
9 5368
 
0.2%
3 1944
 
0.1%
26.5 1660
 
0.1%
2 1385
 
0.1%
0.55 800
 
< 0.1%
0.52 748
 
< 0.1%
24.5 733
 
< 0.1%
0.64 677
 
< 0.1%
Other values (769) 108440
 
4.8%
(Missing) 1988785
88.5%
ValueCountFrequency (%)
0 130144
5.8%
0.01 185
 
< 0.1%
0.03 66
 
< 0.1%
0.04 561
 
< 0.1%
0.05 99
 
< 0.1%
ValueCountFrequency (%)
26.5 1660
0.1%
24.5 733
< 0.1%
14.1 23
 
< 0.1%
14.05 12
 
< 0.1%
13.88 9
 
< 0.1%

AZFW
Real number (ℝ)

MISSING  SKEWED 

Distinct18563
Distinct (%)5.4%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean92442.64631
Minimum12648
Maximum15621068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:27.201692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12648
5-th percentile29891
Q153466
median57589
Q361065
95-th percentile151400
Maximum15621068
Range15608420
Interquartile range (IQR)7599

Descriptive statistics

Standard deviation575636.521
Coefficient of variation (CV)6.226958487
Kurtosis480.5313178
Mean92442.64631
Median Absolute Deviation (MAD)3765
Skewness21.75572416
Sum3.160503146 × 1010
Variance3.313574043 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:27.656204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127386 2271
 
0.1%
43294 1141
 
0.1%
13795 703
 
< 0.1%
29841 700
 
< 0.1%
127355 654
 
< 0.1%
13698 629
 
< 0.1%
29871 561
 
< 0.1%
29901 559
 
< 0.1%
13762 522
 
< 0.1%
13823 520
 
< 0.1%
Other values (18553) 333628
 
14.8%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
12648 8
 
< 0.1%
12665 2
 
< 0.1%
12832 20
< 0.1%
12837 18
< 0.1%
13516 21
< 0.1%
ValueCountFrequency (%)
15621068 3
 
< 0.1%
15210600 14
< 0.1%
15110602 2
 
< 0.1%
15110524 3
 
< 0.1%
15021061 8
< 0.1%

ATOW
Real number (ℝ)

MISSING 

Distinct24974
Distinct (%)7.3%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean124983.6399
Minimum12648
Maximum22105357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:28.534886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12648
5-th percentile29891
Q157175
median65076
Q370098
95-th percentile197141
Maximum22105357
Range22092709
Interquartile range (IQR)12923

Descriptive statistics

Standard deviation917863.087
Coefficient of variation (CV)7.343865866
Kurtosis372.9624398
Mean124983.6399
Median Absolute Deviation (MAD)6366
Skewness19.07738959
Sum4.273040668 × 1010
Variance8.424726465 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:29.247858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43294 991
 
< 0.1%
13795 703
 
< 0.1%
29841 700
 
< 0.1%
13698 629
 
< 0.1%
29871 561
 
< 0.1%
29901 559
 
< 0.1%
13762 522
 
< 0.1%
13823 520
 
< 0.1%
29891 488
 
< 0.1%
29951 481
 
< 0.1%
Other values (24964) 335734
 
14.9%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
12648 8
 
< 0.1%
12665 2
 
< 0.1%
12832 20
< 0.1%
12837 18
< 0.1%
13516 21
< 0.1%
ValueCountFrequency (%)
22105357 15
< 0.1%
21971068 3
 
< 0.1%
21741068 3
 
< 0.1%
21731068 1
 
< 0.1%
21691068 20
< 0.1%

ALAW
Real number (ℝ)

MISSING  SKEWED 

Distinct21967
Distinct (%)6.4%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean102328.6774
Minimum12648
Maximum16310600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:29.900015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12648
5-th percentile29891
Q155766
median61007
Q365575
95-th percentile158953
Maximum16310600
Range16297952
Interquartile range (IQR)9809

Descriptive statistics

Standard deviation668513.142
Coefficient of variation (CV)6.53299895
Kurtosis415.5017466
Mean102328.6774
Median Absolute Deviation (MAD)4888
Skewness20.22151083
Sum3.498494686 × 1010
Variance4.469098211 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:30.625560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43294 991
 
< 0.1%
13795 703
 
< 0.1%
29841 700
 
< 0.1%
13698 629
 
< 0.1%
29871 561
 
< 0.1%
29901 559
 
< 0.1%
13762 522
 
< 0.1%
13823 520
 
< 0.1%
29891 488
 
< 0.1%
29951 481
 
< 0.1%
Other values (21957) 335734
 
14.9%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
12648 8
 
< 0.1%
12665 2
 
< 0.1%
12832 20
< 0.1%
12837 18
< 0.1%
13516 21
< 0.1%
ValueCountFrequency (%)
16310600 34
< 0.1%
16171060 1
 
< 0.1%
16110684 2
 
< 0.1%
16110346 1
 
< 0.1%
16106832 7
 
< 0.1%

ATXW
Real number (ℝ)

MISSING  SKEWED 

Distinct25027
Distinct (%)7.3%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean102186.9804
Minimum12648
Maximum22021068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:31.027891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12648
5-th percentile29891
Q157263
median65318
Q370344.25
95-th percentile197207
Maximum22021068
Range22008420
Interquartile range (IQR)13081.25

Descriptive statistics

Standard deviation668410.1457
Coefficient of variation (CV)6.541049979
Kurtosis776.0091436
Mean102186.9804
Median Absolute Deviation (MAD)6433
Skewness27.11258989
Sum3.493650234 × 1010
Variance4.467721229 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:31.497737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43294 991
 
< 0.1%
13795 703
 
< 0.1%
29841 700
 
< 0.1%
13698 629
 
< 0.1%
29871 561
 
< 0.1%
29901 559
 
< 0.1%
13762 522
 
< 0.1%
13823 520
 
< 0.1%
29891 488
 
< 0.1%
29951 481
 
< 0.1%
Other values (25017) 335734
 
14.9%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
12648 8
 
< 0.1%
12665 2
 
< 0.1%
12832 20
< 0.1%
12837 18
< 0.1%
13516 21
< 0.1%
ValueCountFrequency (%)
22021068 3
 
< 0.1%
21791068 3
 
< 0.1%
21781068 1
 
< 0.1%
21761061 15
< 0.1%
21741068 20
< 0.1%

AFT_LIMIT_ZFW
Real number (ℝ)

MISSING 

Distinct5168
Distinct (%)1.6%
Missing1919273
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean73.95277838
Minimum41.7
Maximum1107.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:31.868571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum41.7
5-th percentile55.05
Q163.63
median71.545
Q377.33
95-th percentile100.73
Maximum1107.89
Range1066.19
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation49.73460376
Coefficient of variation (CV)0.6725183941
Kurtosis401.6241589
Mean73.95277838
Median Absolute Deviation (MAD)6.415
Skewness19.50687231
Sum24332386.86
Variance2473.530811
MonotonicityNot monotonic
2024-06-10T16:49:32.274718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.16 1139
 
0.1%
73.06 1118
 
< 0.1%
59.43 956
 
< 0.1%
72.81 928
 
< 0.1%
59.36 685
 
< 0.1%
72.88 659
 
< 0.1%
73 643
 
< 0.1%
73.11 598
 
< 0.1%
93.24 558
 
< 0.1%
72.93 521
 
< 0.1%
Other values (5158) 321221
 
14.3%
(Missing) 1919273
85.4%
ValueCountFrequency (%)
41.7 420
< 0.1%
41.73 236
< 0.1%
41.82 213
< 0.1%
41.84 4
 
< 0.1%
41.88 121
 
< 0.1%
ValueCountFrequency (%)
1107.89 28
< 0.1%
1107.83 22
< 0.1%
1107.82 6
 
< 0.1%
1107.81 3
 
< 0.1%
1107.77 6
 
< 0.1%

FWD_LIMIT_ZFW
Real number (ℝ)

MISSING  SKEWED 

Distinct2758
Distinct (%)0.8%
Missing1919411
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean34.63136944
Minimum12.49
Maximum1034.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:32.678707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12.49
5-th percentile21.51
Q123.96
median37.43
Q340.4
95-th percentile45.37
Maximum1034.99
Range1022.5
Interquartile range (IQR)16.44

Descriptive statistics

Standard deviation22.02850764
Coefficient of variation (CV)0.6360853757
Kurtosis1755.198377
Mean34.63136944
Median Absolute Deviation (MAD)5.12
Skewness38.68887444
Sum11389841.83
Variance485.2551488
MonotonicityNot monotonic
2024-06-10T16:49:33.076723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.4 85085
 
3.8%
42.54 2318
 
0.1%
39.75 1719
 
0.1%
39.77 1531
 
0.1%
42.55 1280
 
0.1%
39.73 1268
 
0.1%
39.78 1245
 
0.1%
43.09 1242
 
0.1%
39.74 1195
 
0.1%
39.76 1156
 
0.1%
Other values (2748) 230849
 
10.3%
(Missing) 1919411
85.4%
ValueCountFrequency (%)
12.49 4
< 0.1%
12.54 1
 
< 0.1%
12.58 1
 
< 0.1%
12.67 1
 
< 0.1%
12.71 1
 
< 0.1%
ValueCountFrequency (%)
1034.99 5
 
< 0.1%
1034.97 1
 
< 0.1%
1034.96 1
 
< 0.1%
1034.95 42
< 0.1%
1034.94 4
 
< 0.1%

AFT_LIMIT_TOW
Real number (ℝ)

MISSING  SKEWED 

Distinct5411
Distinct (%)1.6%
Missing1919297
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean77.63739023
Minimum41.8
Maximum1120.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:33.379635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum41.8
5-th percentile58.47
Q169.44
median75.93
Q381.16
95-th percentile116.66
Maximum1120.41
Range1078.61
Interquartile range (IQR)11.72

Descriptive statistics

Standard deviation25.9794755
Coefficient of variation (CV)0.3346258216
Kurtosis997.0899026
Mean77.63739023
Median Absolute Deviation (MAD)5.76
Skewness25.58406376
Sum25542856.66
Variance674.933147
MonotonicityNot monotonic
2024-06-10T16:49:33.714220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.16 1124
 
< 0.1%
63.26 1024
 
< 0.1%
72.91 1006
 
< 0.1%
59.53 934
 
< 0.1%
73.1 756
 
< 0.1%
72.98 728
 
< 0.1%
59.46 681
 
< 0.1%
73.21 679
 
< 0.1%
73.03 649
 
< 0.1%
59.47 504
 
< 0.1%
Other values (5401) 320917
 
14.3%
(Missing) 1919297
85.4%
ValueCountFrequency (%)
41.8 420
< 0.1%
41.83 236
< 0.1%
41.92 213
< 0.1%
41.94 4
 
< 0.1%
41.98 121
 
< 0.1%
ValueCountFrequency (%)
1120.41 37
< 0.1%
1120.4 2
 
< 0.1%
1120.36 15
< 0.1%
1087.81 11
 
< 0.1%
1087.78 6
 
< 0.1%

FWD_LIMIT_TOW
Real number (ℝ)

MISSING 

Distinct3541
Distinct (%)1.1%
Missing1919297
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean30.44138771
Minimum6.9
Maximum60.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:34.015209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile12.88
Q124.66
median31.47
Q336.85
95-th percentile45.25
Maximum60.76
Range53.86
Interquartile range (IQR)12.19

Descriptive statistics

Standard deviation8.677793034
Coefficient of variation (CV)0.2850656191
Kurtosis-0.01820774944
Mean30.44138771
Median Absolute Deviation (MAD)5.8
Skewness-0.3488881365
Sum10015277.44
Variance75.30409195
MonotonicityNot monotonic
2024-06-10T16:49:34.676766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.21 1690
 
0.1%
37.29 1672
 
0.1%
37.27 1645
 
0.1%
37.24 1640
 
0.1%
37.28 1570
 
0.1%
37.18 1490
 
0.1%
37.26 1471
 
0.1%
37.17 1391
 
0.1%
36.39 1354
 
0.1%
37.14 1347
 
0.1%
Other values (3531) 313732
 
14.0%
(Missing) 1919297
85.4%
ValueCountFrequency (%)
6.9 3
 
< 0.1%
6.91 9
 
< 0.1%
6.92 33
< 0.1%
6.93 10
 
< 0.1%
6.94 22
< 0.1%
ValueCountFrequency (%)
60.76 11
< 0.1%
60.68 22
< 0.1%
60.22 2
 
< 0.1%
59.92 22
< 0.1%
59.5 1
 
< 0.1%

AFT_LIMIT_LAW
Real number (ℝ)

MISSING 

Distinct992
Distinct (%)2.2%
Missing2202341
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean63.29710562
Minimum48.89
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:35.114871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum48.89
5-th percentile50.16
Q159.24
median62.1
Q370.12
95-th percentile73.31
Maximum117
Range68.11
Interquartile range (IQR)10.88

Descriptive statistics

Standard deviation7.741426918
Coefficient of variation (CV)0.122303016
Kurtosis-0.1970116412
Mean63.29710562
Median Absolute Deviation (MAD)3.21
Skewness0.2791771351
Sum2909008.38
Variance59.92969073
MonotonicityNot monotonic
2024-06-10T16:49:35.531069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.16 978
 
< 0.1%
72.91 921
 
< 0.1%
59.53 866
 
< 0.1%
73.22 843
 
< 0.1%
59.46 609
 
< 0.1%
72.99 555
 
< 0.1%
73.1 534
 
< 0.1%
73.03 503
 
< 0.1%
59.24 442
 
< 0.1%
50.17 423
 
< 0.1%
Other values (982) 39284
 
1.7%
(Missing) 2202341
98.0%
ValueCountFrequency (%)
48.89 41
< 0.1%
49.14 44
< 0.1%
49.94 18
 
< 0.1%
49.95 84
< 0.1%
49.96 83
< 0.1%
ValueCountFrequency (%)
117 1
< 0.1%
88.82 1
< 0.1%
88.75 2
< 0.1%
88.45 1
< 0.1%
88.35 2
< 0.1%

FWD_LIMIT_LAW
Real number (ℝ)

MISSING 

Distinct884
Distinct (%)1.9%
Missing2202075
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean40.07992753
Minimum11.38
Maximum81.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:35.851154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11.38
5-th percentile29.58
Q137.18
median37.57
Q346.22
95-th percentile48.2
Maximum81.94
Range70.56
Interquartile range (IQR)9.04

Descriptive statistics

Standard deviation6.599430472
Coefficient of variation (CV)0.1646567466
Kurtosis1.720895618
Mean40.07992753
Median Absolute Deviation (MAD)7.33
Skewness-1.003298874
Sum1852654.57
Variance43.55248256
MonotonicityNot monotonic
2024-06-10T16:49:36.164836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.48 928
 
< 0.1%
37.32 837
 
< 0.1%
46.41 825
 
< 0.1%
46.24 733
 
< 0.1%
37.36 686
 
< 0.1%
37.37 682
 
< 0.1%
37.33 673
 
< 0.1%
37.18 655
 
< 0.1%
37.14 589
 
< 0.1%
37.35 575
 
< 0.1%
Other values (874) 39041
 
1.7%
(Missing) 2202075
97.9%
ValueCountFrequency (%)
11.38 17
< 0.1%
11.43 11
< 0.1%
11.94 12
< 0.1%
12.28 1
 
< 0.1%
12.3 1
 
< 0.1%
ValueCountFrequency (%)
81.94 1
< 0.1%
52.04 1
< 0.1%
51.91 2
< 0.1%
51.32 1
< 0.1%
51.13 2
< 0.1%

LIZFW
Real number (ℝ)

MISSING  SKEWED 

Distinct5940
Distinct (%)1.7%
Missing1906436
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean54.41371239
Minimum0.95
Maximum1088.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:36.484055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile35.69
Q144.39
median53.36
Q362.19
95-th percentile68.67
Maximum1088.96
Range1088.01
Interquartile range (IQR)17.8

Descriptive statistics

Standard deviation37.01903321
Coefficient of variation (CV)0.6803254471
Kurtosis659.0453792
Mean54.41371239
Median Absolute Deviation (MAD)8.9
Skewness24.54428541
Sum18602034.96
Variance1370.40882
MonotonicityNot monotonic
2024-06-10T16:49:36.792505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.14 2145
 
0.1%
66.29 1237
 
0.1%
53.04 1184
 
0.1%
33.64 1035
 
< 0.1%
66.35 841
 
< 0.1%
67.22 599
 
< 0.1%
66.82 586
 
< 0.1%
62.32 576
 
< 0.1%
65.84 562
 
< 0.1%
64.88 547
 
< 0.1%
Other values (5930) 332551
 
14.8%
(Missing) 1906436
84.8%
ValueCountFrequency (%)
0.95 42
< 0.1%
1.49 45
< 0.1%
2.18 1
 
< 0.1%
7.19 2
 
< 0.1%
7.73 1
 
< 0.1%
ValueCountFrequency (%)
1088.96 2
< 0.1%
1088.12 1
 
< 0.1%
1088.05 1
 
< 0.1%
1088.01 4
< 0.1%
1086.59 1
 
< 0.1%

LITOW
Real number (ℝ)

MISSING  SKEWED 

Distinct6290
Distinct (%)1.8%
Missing1906414
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean55.78168024
Minimum0.95
Maximum1088.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:37.097170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile37.51
Q148.33
median54.9
Q361.37
95-th percentile69.55
Maximum1088.96
Range1088.01
Interquartile range (IQR)13.04

Descriptive statistics

Standard deviation36.89584948
Coefficient of variation (CV)0.6614330964
Kurtosis668.1017581
Mean55.78168024
Median Absolute Deviation (MAD)6.52
Skewness24.85882749
Sum19070919.75
Variance1361.303709
MonotonicityNot monotonic
2024-06-10T16:49:37.419609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.29 1185
 
0.1%
53.04 1057
 
< 0.1%
66.35 835
 
< 0.1%
50.81 635
 
< 0.1%
62.62 612
 
< 0.1%
50.31 575
 
< 0.1%
62.32 569
 
< 0.1%
49.98 560
 
< 0.1%
66.82 555
 
< 0.1%
67.22 546
 
< 0.1%
Other values (6280) 334756
 
14.9%
(Missing) 1906414
84.8%
ValueCountFrequency (%)
0.95 41
< 0.1%
1.22 2
 
< 0.1%
1.23 1
 
< 0.1%
1.49 44
< 0.1%
1.61 2
 
< 0.1%
ValueCountFrequency (%)
1088.96 2
 
< 0.1%
1088.12 1
 
< 0.1%
1088.05 1
 
< 0.1%
1088.01 4
< 0.1%
1069.85 9
< 0.1%

LILAW
Real number (ℝ)

MISSING  SKEWED 

Distinct5521
Distinct (%)1.6%
Missing1906421
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean53.610056
Minimum0.58
Maximum1069.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:37.720248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile35.97
Q145.35
median53.15
Q361.55
95-th percentile67.25
Maximum1069.66
Range1069.08
Interquartile range (IQR)16.2

Descriptive statistics

Standard deviation28.86396127
Coefficient of variation (CV)0.5384057288
Kurtosis1047.921831
Mean53.610056
Median Absolute Deviation (MAD)8.17
Skewness30.19903386
Sum18328098.73
Variance833.1282605
MonotonicityNot monotonic
2024-06-10T16:49:38.030415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.29 1203
 
0.1%
53.04 973
 
< 0.1%
66.35 863
 
< 0.1%
50.28 606
 
< 0.1%
37.44 601
 
< 0.1%
62.32 585
 
< 0.1%
67.22 585
 
< 0.1%
50.81 581
 
< 0.1%
50.59 579
 
< 0.1%
64.88 573
 
< 0.1%
Other values (5511) 334729
 
14.9%
(Missing) 1906421
84.8%
ValueCountFrequency (%)
0.58 1
 
< 0.1%
0.95 41
< 0.1%
0.97 1
 
< 0.1%
1.02 2
 
< 0.1%
1.06 1
 
< 0.1%
ValueCountFrequency (%)
1069.66 3
 
< 0.1%
1069.26 8
< 0.1%
1069.04 2
 
< 0.1%
1068.78 5
< 0.1%
1068.59 2
 
< 0.1%

MAC_AT_ZFW
Real number (ℝ)

MISSING 

Distinct2808
Distinct (%)0.8%
Missing1906499
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean25.07763725
Minimum1.56
Maximum48.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:38.344502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.56
5-th percentile16.18
Q120.46
median25.83
Q329.56
95-th percentile32.96
Maximum48.33
Range46.77
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation5.471490607
Coefficient of variation (CV)0.2181820621
Kurtosis-0.9467288606
Mean25.07763725
Median Absolute Deviation (MAD)4.5
Skewness-0.1932355654
Sum8571536.411
Variance29.93720947
MonotonicityNot monotonic
2024-06-10T16:49:38.656354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.92 2449
 
0.1%
20.51 1183
 
0.1%
27.13 1154
 
0.1%
27.16 1135
 
0.1%
26.45 946
 
< 0.1%
27.19 942
 
< 0.1%
27.17 818
 
< 0.1%
25.79 787
 
< 0.1%
25.44 715
 
< 0.1%
27.06 699
 
< 0.1%
Other values (2798) 330972
 
14.7%
(Missing) 1906499
84.8%
ValueCountFrequency (%)
1.56 2
< 0.1%
1.6 1
< 0.1%
1.67 1
< 0.1%
1.7 1
< 0.1%
1.72 1
< 0.1%
ValueCountFrequency (%)
48.33 1
< 0.1%
47.76 2
< 0.1%
46.71 1
< 0.1%
46.32 1
< 0.1%
44.19 1
< 0.1%

MAC_AT_TOW
Real number (ℝ)

MISSING 

Distinct2528
Distinct (%)0.7%
Missing1906499
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean25.14378398
Minimum3.15
Maximum46.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:38.951895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.15
5-th percentile16.62
Q121.96
median25.76
Q328.64
95-th percentile31.51
Maximum46.82
Range43.67
Interquartile range (IQR)6.68

Descriptive statistics

Standard deviation4.502551055
Coefficient of variation (CV)0.179072134
Kurtosis-0.4504570885
Mean25.14378398
Median Absolute Deviation (MAD)3.24
Skewness-0.4102622217
Sum8594145.366
Variance20.272966
MonotonicityNot monotonic
2024-06-10T16:49:39.250358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.16 1252
 
0.1%
27.13 1102
 
< 0.1%
26.45 1069
 
< 0.1%
27.19 994
 
< 0.1%
27.17 909
 
< 0.1%
23.34 906
 
< 0.1%
25.44 887
 
< 0.1%
27.06 855
 
< 0.1%
25.79 752
 
< 0.1%
25.48 716
 
< 0.1%
Other values (2518) 332358
 
14.8%
(Missing) 1906499
84.8%
ValueCountFrequency (%)
3.15 1
< 0.1%
5.06 1
< 0.1%
5.57 1
< 0.1%
6.2 2
< 0.1%
6.23 1
< 0.1%
ValueCountFrequency (%)
46.82 2
< 0.1%
44.19 1
< 0.1%
40.4 1
< 0.1%
39.98 1
< 0.1%
39.78 1
< 0.1%

MAC_AT_LAW
Real number (ℝ)

MISSING 

Distinct2711
Distinct (%)0.8%
Missing1906499
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean24.98050599
Minimum3.15
Maximum47.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:39.530311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.15
5-th percentile16.49
Q120.85
median25.54
Q328.94
95-th percentile32.76
Maximum47.2
Range44.05
Interquartile range (IQR)8.09

Descriptive statistics

Standard deviation5.129935439
Coefficient of variation (CV)0.2053575472
Kurtosis-0.7924373635
Mean24.98050599
Median Absolute Deviation (MAD)4.04
Skewness-0.1526362688
Sum8538336.948
Variance26.31623761
MonotonicityNot monotonic
2024-06-10T16:49:39.883276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.16 1125
 
0.1%
26.45 1005
 
< 0.1%
27.13 983
 
< 0.1%
27.17 914
 
< 0.1%
27.19 890
 
< 0.1%
25.44 855
 
< 0.1%
25.48 724
 
< 0.1%
25.79 703
 
< 0.1%
25.27 674
 
< 0.1%
26.93 653
 
< 0.1%
Other values (2701) 333274
 
14.8%
(Missing) 1906499
84.8%
ValueCountFrequency (%)
3.15 1
< 0.1%
3.18 1
< 0.1%
3.19 1
< 0.1%
3.23 1
< 0.1%
3.29 1
< 0.1%
ValueCountFrequency (%)
47.2 2
< 0.1%
44.19 1
< 0.1%
43.4 1
< 0.1%
42.66 1
< 0.1%
41.82 1
< 0.1%

DEADLOAD_MAC
Real number (ℝ)

MISSING 

Distinct2471
Distinct (%)0.9%
Missing1965219
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean22.19237135
Minimum0.84
Maximum44.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:40.431543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.84
5-th percentile15.44
Q118.48
median21.94
Q326.48
95-th percentile29.15
Maximum44.07
Range43.23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.639977038
Coefficient of variation (CV)0.2090798214
Kurtosis-0.7976397278
Mean22.19237135
Median Absolute Deviation (MAD)4.12
Skewness-0.08953254052
Sum6282216.481
Variance21.52938691
MonotonicityNot monotonic
2024-06-10T16:49:40.795033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.13 11886
 
0.5%
26.91 5901
 
0.3%
20.92 2965
 
0.1%
24.65 2644
 
0.1%
20.51 1771
 
0.1%
28.83 1656
 
0.1%
24.99 1515
 
0.1%
26.06 1479
 
0.1%
15.78 1379
 
0.1%
25.47 1353
 
0.1%
Other values (2461) 250531
 
11.1%
(Missing) 1965219
87.4%
ValueCountFrequency (%)
0.84 1
< 0.1%
0.95 1
< 0.1%
0.97 2
< 0.1%
1 1
< 0.1%
1.01 1
< 0.1%
ValueCountFrequency (%)
44.07 1
< 0.1%
43.93 1
< 0.1%
43.38 1
< 0.1%
42.17 1
< 0.1%
40.72 1
< 0.1%

UNDERLOAD
Real number (ℝ)

MISSING 

Distinct15416
Distinct (%)5.5%
Missing1965498
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean23328.40043
Minimum1
Maximum4761045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:41.131330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2094
Q14683
median6720
Q310263
95-th percentile26392
Maximum4761045
Range4761044
Interquartile range (IQR)5580

Descriptive statistics

Standard deviation240644.0552
Coefficient of variation (CV)10.31549745
Kurtosis348.1713833
Mean23328.40043
Median Absolute Deviation (MAD)2426
Skewness18.42579986
Sum6597294969
Variance5.790956128 × 1010
MonotonicityNot monotonic
2024-06-10T16:49:41.448010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47614 2271
 
0.1%
19206 1141
 
0.1%
4761045 629
 
< 0.1%
21575 420
 
< 0.1%
47645 351
 
< 0.1%
21206 341
 
< 0.1%
21178 253
 
< 0.1%
21086 248
 
< 0.1%
21001 246
 
< 0.1%
21554 236
 
< 0.1%
Other values (15406) 276665
 
12.3%
(Missing) 1965498
87.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
6 5
 
< 0.1%
7 6
< 0.1%
8 5
 
< 0.1%
11 14
< 0.1%
ValueCountFrequency (%)
4761045 629
< 0.1%
3721089 16
 
< 0.1%
3501089 2
 
< 0.1%
3410896 13
 
< 0.1%
3110444 4
 
< 0.1%

LIMITING_WEIGHT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2248299
Missing (%)100.0%
Memory size34.3 MiB

ALLOWED TOW
Real number (ℝ)

MISSING  SKEWED 

Distinct2644
Distinct (%)0.8%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean97407.23218
Minimum20
Maximum22251030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:41.764526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile42500
Q165952
median71297
Q374288
95-th percentile217960
Maximum22251030
Range22251010
Interquartile range (IQR)8336

Descriptive statistics

Standard deviation473476.9786
Coefficient of variation (CV)4.860799019
Kurtosis2096.606725
Mean97407.23218
Median Absolute Deviation (MAD)3698
Skewness45.18755629
Sum3.33023638 × 1010
Variance2.241804493 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:42.114525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65952 23271
 
1.0%
64300 22006
 
1.0%
42500 18286
 
0.8%
62500 13485
 
0.6%
75600 8907
 
0.4%
73500 8554
 
0.4%
21000 8237
 
0.4%
97000 5380
 
0.2%
51850 4401
 
0.2%
175000 4261
 
0.2%
Other values (2634) 225100
 
10.0%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
20 1
 
< 0.1%
8958 2
 
< 0.1%
11299 1
 
< 0.1%
20000 20
 
< 0.1%
20800 1936
0.1%
ValueCountFrequency (%)
22251030 86
< 0.1%
22141088 41
< 0.1%
21710458 26
 
< 0.1%
2221133 48
< 0.1%
2113769 1
 
< 0.1%

ALLOWED ZFW
Real number (ℝ)

MISSING  SKEWED 

Distinct18341
Distinct (%)5.4%
Missing1906412
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean88068.92683
Minimum2967
Maximum1105210525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:42.431272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2967
5-th percentile30116
Q153826
median57882
Q361085
95-th percentile151817
Maximum1105210525
Range1105207558
Interquartile range (IQR)7259

Descriptive statistics

Standard deviation1951442.159
Coefficient of variation (CV)22.15812352
Kurtosis300846.4722
Mean88068.92683
Median Absolute Deviation (MAD)3598
Skewness531.6440864
Sum3.010962119 × 1010
Variance3.808126499 × 1012
MonotonicityNot monotonic
2024-06-10T16:49:42.948032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127886 2271
 
0.1%
43794 1169
 
0.1%
127855 980
 
< 0.1%
13905 703
 
< 0.1%
30066 700
 
< 0.1%
13808 629
 
< 0.1%
30096 561
 
< 0.1%
30126 559
 
< 0.1%
13872 522
 
< 0.1%
13933 520
 
< 0.1%
Other values (18331) 333273
 
14.8%
(Missing) 1906412
84.8%
ValueCountFrequency (%)
2967 1
 
< 0.1%
5308 1
 
< 0.1%
8958 1
 
< 0.1%
12758 8
< 0.1%
12775 2
 
< 0.1%
ValueCountFrequency (%)
1105210525 1
 
< 0.1%
15671068 3
 
< 0.1%
15410681 2
 
< 0.1%
15410606 25
< 0.1%
15310528 5
 
< 0.1%

ALLOWED LAW
Real number (ℝ)

MISSING  SKEWED 

Distinct21749
Distinct (%)6.4%
Missing1906412
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean109116.8972
Minimum8958
Maximum1105210525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:43.298213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8958
5-th percentile30116
Q156175
median61340
Q365592
95-th percentile159452
Maximum1105210525
Range1105201567
Interquartile range (IQR)9417

Descriptive statistics

Standard deviation2014451.381
Coefficient of variation (CV)18.46140637
Kurtosis264918.134
Mean109116.8972
Median Absolute Deviation (MAD)4687
Skewness483.6973484
Sum3.730564865 × 1010
Variance4.058014368 × 1012
MonotonicityNot monotonic
2024-06-10T16:49:43.631319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43794 1019
 
< 0.1%
13905 703
 
< 0.1%
30066 700
 
< 0.1%
13808 629
 
< 0.1%
30096 561
 
< 0.1%
30126 559
 
< 0.1%
13872 522
 
< 0.1%
13933 520
 
< 0.1%
30116 488
 
< 0.1%
30176 481
 
< 0.1%
Other values (21739) 335705
 
14.9%
(Missing) 1906412
84.8%
ValueCountFrequency (%)
8958 3
 
< 0.1%
12758 8
 
< 0.1%
12775 2
 
< 0.1%
12942 20
< 0.1%
12947 18
< 0.1%
ValueCountFrequency (%)
1105210525 1
 
< 0.1%
16221060 1
 
< 0.1%
16110696 11
< 0.1%
16110535 25
< 0.1%
16106825 20
< 0.1%

ALLOWED TXW
Real number (ℝ)

MISSING  SKEWED 

Distinct24840
Distinct (%)7.3%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean126947.3738
Minimum460
Maximum1105210525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:43.990572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum460
5-th percentile30116
Q157491
median65551
Q370468
95-th percentile198317
Maximum1105210525
Range1105210065
Interquartile range (IQR)12977

Descriptive statistics

Standard deviation2094297.974
Coefficient of variation (CV)16.4973714
Kurtosis226762.654
Mean126947.3738
Median Absolute Deviation (MAD)6384
Skewness431.2761542
Sum4.340178373 × 1010
Variance4.386084004 × 1012
MonotonicityNot monotonic
2024-06-10T16:49:44.364609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43794 1019
 
< 0.1%
13905 703
 
< 0.1%
30066 700
 
< 0.1%
13808 629
 
< 0.1%
30096 561
 
< 0.1%
30126 559
 
< 0.1%
13872 522
 
< 0.1%
13933 520
 
< 0.1%
30116 488
 
< 0.1%
30176 481
 
< 0.1%
Other values (24830) 335706
 
14.9%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
460 1
 
< 0.1%
8958 1
 
< 0.1%
9167 1
 
< 0.1%
11508 1
 
< 0.1%
12758 8
< 0.1%
ValueCountFrequency (%)
1105210525 1
 
< 0.1%
22071068 3
 
< 0.1%
21841068 3
 
< 0.1%
21831068 1
 
< 0.1%
21811061 15
< 0.1%

STABTO
Real number (ℝ)

MISSING 

Distinct651
Distinct (%)0.8%
Missing2164914
Missing (%)96.3%
Infinite0
Infinite (%)0.0%
Mean1.792912471
Minimum0
Maximum6.71
Zeros272
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:44.915585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.29
median0.9
Q33.21
95-th percentile5.55
Maximum6.71
Range6.71
Interquartile range (IQR)2.92

Descriptive statistics

Standard deviation1.796311499
Coefficient of variation (CV)1.001895814
Kurtosis-0.5432257517
Mean1.792912471
Median Absolute Deviation (MAD)0.78
Skewness0.8476078974
Sum149502.0064
Variance3.226735002
MonotonicityNot monotonic
2024-06-10T16:49:45.290356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 998
 
< 0.1%
0.06 960
 
< 0.1%
0.12 918
 
< 0.1%
0.03 865
 
< 0.1%
0.17 855
 
< 0.1%
0.13 837
 
< 0.1%
0.21 836
 
< 0.1%
0.08 785
 
< 0.1%
0.07 785
 
< 0.1%
0.19 765
 
< 0.1%
Other values (641) 74781
 
3.3%
(Missing) 2164914
96.3%
ValueCountFrequency (%)
0 272
 
< 0.1%
0.01 648
< 0.1%
0.02 638
< 0.1%
0.03 865
< 0.1%
0.04 998
< 0.1%
ValueCountFrequency (%)
6.71 2
 
< 0.1%
6.58 2
 
< 0.1%
6.52 136
< 0.1%
6.49 1
 
< 0.1%
6.45 3
 
< 0.1%

OPTIMAL_TRIM
Real number (ℝ)

MISSING  SKEWED 

Distinct4520
Distinct (%)1.4%
Missing1919480
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean61.34877156
Minimum29.8
Maximum1089.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:45.648019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum29.8
5-th percentile45.04
Q153.52
median59.48
Q362.88
95-th percentile97.18
Maximum1089.99
Range1060.19
Interquartile range (IQR)9.36

Descriptive statistics

Standard deviation25.99611761
Coefficient of variation (CV)0.4237430832
Kurtosis1105.795588
Mean61.34877156
Median Absolute Deviation (MAD)5.01
Skewness28.22627342
Sum20172641.72
Variance675.798131
MonotonicityNot monotonic
2024-06-10T16:49:46.064597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.49 2494
 
0.1%
59.5 1704
 
0.1%
66.33 1582
 
0.1%
50.88 1000
 
< 0.1%
66.35 960
 
< 0.1%
66.27 921
 
< 0.1%
63.03 855
 
< 0.1%
66.32 813
 
< 0.1%
62.99 797
 
< 0.1%
62.56 778
 
< 0.1%
Other values (4510) 316915
 
14.1%
(Missing) 1919480
85.4%
ValueCountFrequency (%)
29.8 420
< 0.1%
29.83 236
< 0.1%
29.92 213
< 0.1%
29.94 4
 
< 0.1%
29.98 121
 
< 0.1%
ValueCountFrequency (%)
1089.99 5
 
< 0.1%
1089.95 2
 
< 0.1%
1089.5 27
< 0.1%
1089.47 12
< 0.1%
1089.38 2
 
< 0.1%

IDEAL_ADDITIONAL_LOAD_AFT
Real number (ℝ)

MISSING  SKEWED 

Distinct56865
Distinct (%)26.4%
Missing2032810
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean1926.05178
Minimum0.07
Maximum1110340.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:46.614341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile72.494
Q1357.51
median771.78
Q31746.75
95-th percentile5407.88
Maximum1110340.84
Range1110340.77
Interquartile range (IQR)1389.24

Descriptive statistics

Standard deviation13410.21943
Coefficient of variation (CV)6.962543566
Kurtosis1640.388157
Mean1926.05178
Median Absolute Deviation (MAD)530.34
Skewness35.33252022
Sum415042972
Variance179833985.1
MonotonicityNot monotonic
2024-06-10T16:49:47.089325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5485.24 558
 
< 0.1%
5329.62 390
 
< 0.1%
5015.92 336
 
< 0.1%
4805.72 336
 
< 0.1%
4812.49 332
 
< 0.1%
5243.25 328
 
< 0.1%
5116.1 324
 
< 0.1%
4921.95 262
 
< 0.1%
5042.46 156
 
< 0.1%
5428.37 136
 
< 0.1%
Other values (56855) 212331
 
9.4%
(Missing) 2032810
90.4%
ValueCountFrequency (%)
0.07 2
 
< 0.1%
0.09 20
< 0.1%
0.1 16
< 0.1%
0.14 1
 
< 0.1%
0.15 2
 
< 0.1%
ValueCountFrequency (%)
1110340.84 3
< 0.1%
910347.59 1
 
< 0.1%
711061.75 3
< 0.1%
710520.84 3
< 0.1%
710307.62 1
 
< 0.1%

IDEAL_ADDITIONAL_LOAD_FWD
Real number (ℝ)

MISSING  SKEWED 

Distinct20818
Distinct (%)20.0%
Missing2144362
Missing (%)95.4%
Infinite0
Infinite (%)0.0%
Mean486.2157405
Minimum0.04
Maximum171087.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:47.563589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile32.68
Q1164.13
median344.14
Q3582.26
95-th percentile1405.162
Maximum171087.31
Range171087.27
Interquartile range (IQR)418.13

Descriptive statistics

Standard deviation2004.020884
Coefficient of variation (CV)4.121670107
Kurtosis6295.275447
Mean486.2157405
Median Absolute Deviation (MAD)199.46
Skewness76.03918959
Sum50535805.42
Variance4016099.702
MonotonicityNot monotonic
2024-06-10T16:49:47.895432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.42 770
 
< 0.1%
442.24 420
 
< 0.1%
504 341
 
< 0.1%
518.48 255
 
< 0.1%
391.71 246
 
< 0.1%
485.06 238
 
< 0.1%
396.92 237
 
< 0.1%
523.9 229
 
< 0.1%
582.26 213
 
< 0.1%
501.65 211
 
< 0.1%
Other values (20808) 100777
 
4.5%
(Missing) 2144362
95.4%
ValueCountFrequency (%)
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.08 4
< 0.1%
0.09 5
< 0.1%
0.15 1
 
< 0.1%
ValueCountFrequency (%)
171087.31 11
< 0.1%
171087.25 1
 
< 0.1%
111087.9 2
 
< 0.1%
81086.46 2
 
< 0.1%
51087.47 1
 
< 0.1%

TAIL_TIPPING_WI weight
Real number (ℝ)

MISSING  SKEWED 

Distinct35243
Distinct (%)10.3%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean98336.41954
Minimum12648
Maximum1104431044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:48.214683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12648
5-th percentile29871
Q149643.9625
median54486
Q361648.7225
95-th percentile175232
Maximum1104431044
Range1104418396
Interquartile range (IQR)12004.76

Descriptive statistics

Standard deviation2736154.64
Coefficient of variation (CV)27.82442815
Kurtosis155234.3804
Mean98336.41954
Median Absolute Deviation (MAD)6377
Skewness384.8702935
Sum3.36200418 × 1010
Variance7.486542214 × 1012
MonotonicityNot monotonic
2024-06-10T16:49:48.597712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43294 1108
 
< 0.1%
29841 1016
 
< 0.1%
29901 857
 
< 0.1%
29871 805
 
< 0.1%
13795 772
 
< 0.1%
13698 695
 
< 0.1%
29951 666
 
< 0.1%
29839 666
 
< 0.1%
29884 658
 
< 0.1%
29891 620
 
< 0.1%
Other values (35233) 334025
 
14.9%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
12648 8
 
< 0.1%
12665 3
 
< 0.1%
12832 20
< 0.1%
12837 18
< 0.1%
13516 21
< 0.1%
ValueCountFrequency (%)
1104431044 2
 
< 0.1%
21281069 2
 
< 0.1%
20710612.52 1
 
< 0.1%
20651069 17
< 0.1%
20611060 14
< 0.1%

TAIL_TIPPING_WI index
Real number (ℝ)

MISSING 

Distinct7548
Distinct (%)2.2%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean84.87258292
Minimum0.95
Maximum1127.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:48.929472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile41.21
Q168.47
median86.05
Q396.26
95-th percentile108.62
Maximum1127.83
Range1126.88
Interquartile range (IQR)27.79

Descriptive statistics

Standard deviation58.82157646
Coefficient of variation (CV)0.6930574567
Kurtosis251.163341
Mean84.87258292
Median Absolute Deviation (MAD)12.33
Skewness14.90529805
Sum29016917.63
Variance3459.977857
MonotonicityNot monotonic
2024-06-10T16:49:49.248085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.29 1294
 
0.1%
53.04 938
 
< 0.1%
66.35 898
 
< 0.1%
66.82 697
 
< 0.1%
42.08 690
 
< 0.1%
62.32 610
 
< 0.1%
67.22 608
 
< 0.1%
64.91 586
 
< 0.1%
65.92 572
 
< 0.1%
64.88 562
 
< 0.1%
Other values (7538) 334433
 
14.9%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
0.95 42
< 0.1%
1.49 45
< 0.1%
21.53 1
 
< 0.1%
21.63 1
 
< 0.1%
22.56 9
 
< 0.1%
ValueCountFrequency (%)
1127.83 1
 
< 0.1%
1127.13 3
 
< 0.1%
1125.58 15
< 0.1%
1123.57 6
 
< 0.1%
1123.28 8
< 0.1%

TAIL_TIPPING_INDEX_EXCEEDED
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2248299
Missing (%)100.0%
Memory size34.3 MiB

FWD_MOVABLE_PAX
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2248299
Missing (%)100.0%
Memory size34.3 MiB

AFT_MOVABLE_PAX
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2248299
Missing (%)100.0%
Memory size34.3 MiB

INDEX_OUT_OF_BALANCE
Real number (ℝ)

MISSING 

Distinct1049
Distinct (%)4.5%
Missing2225151
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean8.214976672
Minimum0.03
Maximum39.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:49.697809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile1.26
Q14.54
median7.4
Q311.13
95-th percentile16.75
Maximum39.73
Range39.7
Interquartile range (IQR)6.59

Descriptive statistics

Standard deviation4.879978663
Coefficient of variation (CV)0.5940343909
Kurtosis-0.1978330546
Mean8.214976672
Median Absolute Deviation (MAD)2.91
Skewness0.6043130156
Sum190160.28
Variance23.81419175
MonotonicityNot monotonic
2024-06-10T16:49:50.097185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4 2278
 
0.1%
8.91 980
 
< 0.1%
9.14 409
 
< 0.1%
3.59 407
 
< 0.1%
7.97 359
 
< 0.1%
3.85 359
 
< 0.1%
5.13 342
 
< 0.1%
5.41 336
 
< 0.1%
4.54 329
 
< 0.1%
6.43 329
 
< 0.1%
Other values (1039) 17020
 
0.8%
(Missing) 2225151
99.0%
ValueCountFrequency (%)
0.03 55
< 0.1%
0.04 1
 
< 0.1%
0.05 7
 
< 0.1%
0.06 13
 
< 0.1%
0.07 3
 
< 0.1%
ValueCountFrequency (%)
39.73 1
< 0.1%
38.1 1
< 0.1%
35.52 1
< 0.1%
28.96 1
< 0.1%
28.87 1
< 0.1%

LOAD_TO_AFT
Real number (ℝ)

MISSING 

Distinct528
Distinct (%)7.7%
Missing2241481
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean2812.661088
Minimum4.33
Maximum181035.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:50.482833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.33
5-th percentile102.48
Q1553.71
median1679.28
Q31679.28
95-th percentile2021.94
Maximum181035.86
Range181031.53
Interquartile range (IQR)1125.57

Descriptive statistics

Standard deviation16528.80817
Coefficient of variation (CV)5.876572986
Kurtosis112.1075441
Mean2812.661088
Median Absolute Deviation (MAD)342.66
Skewness10.66801691
Sum19176723.3
Variance273201499.6
MonotonicityNot monotonic
2024-06-10T16:49:50.881747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1679.28 2271
 
0.1%
2021.94 980
 
< 0.1%
1878.98 166
 
< 0.1%
2221.64 76
 
< 0.1%
208.78 75
 
< 0.1%
1041.61 75
 
< 0.1%
331.32 71
 
< 0.1%
592.29 69
 
< 0.1%
717.1 63
 
< 0.1%
27.23 62
 
< 0.1%
Other values (518) 2910
 
0.1%
(Missing) 2241481
99.7%
ValueCountFrequency (%)
4.33 1
 
< 0.1%
5.77 1
 
< 0.1%
6.81 54
< 0.1%
7.16 2
 
< 0.1%
7.22 1
 
< 0.1%
ValueCountFrequency (%)
181035.86 58
< 0.1%
10696.67 12
 
< 0.1%
4166.92 1
 
< 0.1%
4148.16 2
 
< 0.1%
4093.31 1
 
< 0.1%

LOAD_TO_FWD
Real number (ℝ)

MISSING 

Distinct269
Distinct (%)46.5%
Missing2247720
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean328.993886
Minimum5.53
Maximum11086.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:51.247825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.53
5-th percentile29.157
Q179.67
median232.59
Q3426.99
95-th percentile936.429
Maximum11086.29
Range11080.76
Interquartile range (IQR)347.32

Descriptive statistics

Standard deviation556.8698299
Coefficient of variation (CV)1.69264492
Kurtosis242.9544553
Mean328.993886
Median Absolute Deviation (MAD)155.37
Skewness13.12344527
Sum190487.46
Variance310104.0075
MonotonicityNot monotonic
2024-06-10T16:49:51.564523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.22 29
 
< 0.1%
299.72 24
 
< 0.1%
108.11 16
 
< 0.1%
108.82 14
 
< 0.1%
340.3 11
 
< 0.1%
240.7 11
 
< 0.1%
252.82 10
 
< 0.1%
30.7 8
 
< 0.1%
19.37 7
 
< 0.1%
41.03 6
 
< 0.1%
Other values (259) 443
 
< 0.1%
(Missing) 2247720
> 99.9%
ValueCountFrequency (%)
5.53 6
< 0.1%
7.6 4
< 0.1%
9.45 1
 
< 0.1%
11.07 5
< 0.1%
15.44 1
 
< 0.1%
ValueCountFrequency (%)
11086.29 1
< 0.1%
3392.1 1
< 0.1%
2611.67 1
< 0.1%
1968.2 1
< 0.1%
1507.73 1
< 0.1%

ESTIMATED_TRAFFIC_LOAD
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct24847
Distinct (%)8.1%
Missing1942718
Missing (%)86.4%
Infinite0
Infinite (%)0.0%
Mean15743.74647
Minimum0
Maximum2710681
Zeros32386
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:51.930951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111491
median14015
Q315708
95-th percentile25740
Maximum2710681
Range2710681
Interquartile range (IQR)4217

Descriptive statistics

Standard deviation64866.18505
Coefficient of variation (CV)4.120123832
Kurtosis1060.994074
Mean15743.74647
Median Absolute Deviation (MAD)2003
Skewness31.66180485
Sum4810989791
Variance4207621963
MonotonicityNot monotonic
2024-06-10T16:49:52.630920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32386
 
1.4%
14008 264
 
< 0.1%
13575 248
 
< 0.1%
13486 236
 
< 0.1%
14912 233
 
< 0.1%
14507 210
 
< 0.1%
14824 200
 
< 0.1%
12950 199
 
< 0.1%
13910 199
 
< 0.1%
12031 196
 
< 0.1%
Other values (24837) 271210
 
12.1%
(Missing) 1942718
86.4%
ValueCountFrequency (%)
0 32386
1.4%
1.78 10
 
< 0.1%
2.7 3
 
< 0.1%
4.2 7
 
< 0.1%
8.56 2
 
< 0.1%
ValueCountFrequency (%)
2710681 21
< 0.1%
2601052 2
 
< 0.1%
2511053 18
< 0.1%
2411034 3
 
< 0.1%
2310534 17
< 0.1%

ESTIMATED_ZFW
Real number (ℝ)

MISSING  SKEWED 

Distinct29886
Distinct (%)8.7%
Missing1906411
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean92195.80308
Minimum12648
Maximum15621068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:53.065853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12648
5-th percentile29891
Q153818
median57971
Q361406.5
95-th percentile152288
Maximum15621068
Range15608420
Interquartile range (IQR)7588.5

Descriptive statistics

Standard deviation573632.8198
Coefficient of variation (CV)6.221897317
Kurtosis510.1258426
Mean92195.80308
Median Absolute Deviation (MAD)3745
Skewness22.40642059
Sum3.152063872 × 1010
Variance3.29054612 × 1011
MonotonicityNot monotonic
2024-06-10T16:49:53.414316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127386 2271
 
0.1%
43294 983
 
< 0.1%
13795 714
 
< 0.1%
29841 700
 
< 0.1%
127355 654
 
< 0.1%
13698 629
 
< 0.1%
29951 605
 
< 0.1%
29871 581
 
< 0.1%
29901 559
 
< 0.1%
13762 522
 
< 0.1%
Other values (29876) 333670
 
14.8%
(Missing) 1906411
84.8%
ValueCountFrequency (%)
12648 8
 
< 0.1%
12665 1
 
< 0.1%
12832 20
< 0.1%
12837 18
< 0.1%
13516 21
< 0.1%
ValueCountFrequency (%)
15621068 3
 
< 0.1%
15210600 14
< 0.1%
15191034 1
 
< 0.1%
15110602 5
 
< 0.1%
15110524 3
 
< 0.1%

DELTA_ZFW
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3573
Distinct (%)1.9%
Missing2061504
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean356.1927219
Minimum0
Maximum1107050
Zeros103275
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:53.764387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3236
95-th percentile1282
Maximum1107050
Range1107050
Interquartile range (IQR)236

Descriptive statistics

Standard deviation5033.825897
Coefficient of variation (CV)14.13230981
Kurtosis27466.15755
Mean356.1927219
Median Absolute Deviation (MAD)0
Skewness146.3950883
Sum66535019.49
Variance25339403.16
MonotonicityNot monotonic
2024-06-10T16:49:54.097824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103275
 
4.6%
88 2828
 
0.1%
85 2610
 
0.1%
70 2143
 
0.1%
158 1975
 
0.1%
18 1508
 
0.1%
176 1006
 
< 0.1%
75 975
 
< 0.1%
228 946
 
< 0.1%
246 883
 
< 0.1%
Other values (3563) 68646
 
3.1%
(Missing) 2061504
91.7%
ValueCountFrequency (%)
0 103275
4.6%
0.11 13
 
< 0.1%
0.2 5
 
< 0.1%
0.22 20
 
< 0.1%
0.29 1
 
< 0.1%
ValueCountFrequency (%)
1107050 2
 
< 0.1%
381068 14
< 0.1%
110451 1
 
< 0.1%
55667 5
 
< 0.1%
52904 2
 
< 0.1%

ZFW_TOLERANCE_EXCEEDED
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2248299
Missing (%)100.0%
Memory size34.3 MiB

Total bag weight
Real number (ℝ)

MISSING  SKEWED 

Distinct2326
Distinct (%)0.9%
Missing1996812
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean3113.198531
Minimum0
Maximum10301030
Zeros14310
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:54.414372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1975
median1391
Q31820
95-th percentile3150
Maximum10301030
Range10301030
Interquartile range (IQR)845

Descriptive statistics

Standard deviation123416.2301
Coefficient of variation (CV)39.64290388
Kurtosis6936.184997
Mean3113.198531
Median Absolute Deviation (MAD)429
Skewness83.16872844
Sum782928959
Variance1.523156586 × 1010
MonotonicityNot monotonic
2024-06-10T16:49:54.764224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14310
 
0.6%
1820 8749
 
0.4%
1365 5679
 
0.3%
12 3921
 
0.2%
910 2788
 
0.1%
1040 2477
 
0.1%
1105 2344
 
0.1%
1500 2185
 
0.1%
24 1943
 
0.1%
1378 1809
 
0.1%
Other values (2316) 205282
 
9.1%
(Missing) 1996812
88.8%
ValueCountFrequency (%)
0 14310
0.6%
12 3921
 
0.2%
19 5
 
< 0.1%
24 1943
 
0.1%
32 13
 
< 0.1%
ValueCountFrequency (%)
10301030 36
< 0.1%
331060 3
 
< 0.1%
310600 68
< 0.1%
310300 3
 
< 0.1%
241060 84
< 0.1%
Distinct183
Distinct (%)< 0.1%
Missing46
Missing (%)< 0.1%
Memory size34.3 MiB
2024-06-10T16:49:55.414258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length72
Median length65
Mean length27.2709357
Min length12

Characters and Unicode

Total characters61311963
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBagdogra Airport
2nd rowBagdogra Airport
3rd rowBagdogra Airport
4th rowBagdogra Airport
5th rowBagdogra Airport
ValueCountFrequency (%)
airport 2249670
30.2%
international 1061980
14.3%
dublin 839768
 
11.3%
kempegowda 149219
 
2.0%
shivaji 146928
 
2.0%
chhatrapati 146928
 
2.0%
139681
 
1.9%
gandhi 102566
 
1.4%
viracopos 84621
 
1.1%
indira 79922
 
1.1%
Other values (382) 2437939
32.8%
2024-06-10T16:49:56.630859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 7077012
11.5%
i 5649217
 
9.2%
t 5260189
 
8.6%
n 5253880
 
8.6%
5190969
 
8.5%
o 5099157
 
8.3%
a 5058356
 
8.3%
p 2812292
 
4.6%
e 2692677
 
4.4%
l 2498197
 
4.1%
Other values (58) 14720017
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61311963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 7077012
11.5%
i 5649217
 
9.2%
t 5260189
 
8.6%
n 5253880
 
8.6%
5190969
 
8.5%
o 5099157
 
8.3%
a 5058356
 
8.3%
p 2812292
 
4.6%
e 2692677
 
4.4%
l 2498197
 
4.1%
Other values (58) 14720017
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61311963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 7077012
11.5%
i 5649217
 
9.2%
t 5260189
 
8.6%
n 5253880
 
8.6%
5190969
 
8.5%
o 5099157
 
8.3%
a 5058356
 
8.3%
p 2812292
 
4.6%
e 2692677
 
4.4%
l 2498197
 
4.1%
Other values (58) 14720017
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61311963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 7077012
11.5%
i 5649217
 
9.2%
t 5260189
 
8.6%
n 5253880
 
8.6%
5190969
 
8.5%
o 5099157
 
8.3%
a 5058356
 
8.3%
p 2812292
 
4.6%
e 2692677
 
4.4%
l 2498197
 
4.1%
Other values (58) 14720017
24.0%

city
Text

Distinct181
Distinct (%)< 0.1%
Missing46
Missing (%)< 0.1%
Memory size34.3 MiB
2024-06-10T16:49:57.316004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length24
Median length6
Mean length7.258708428
Min length3

Characters and Unicode

Total characters16319413
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSiliguri
2nd rowSiliguri
3rd rowSiliguri
4th rowSiliguri
5th rowSiliguri
ValueCountFrequency (%)
dublin 839768
32.4%
bangalore 149219
 
5.8%
mumbai 146928
 
5.7%
new 113432
 
4.4%
campinas 84621
 
3.3%
delhi 79922
 
3.1%
mopa 58495
 
2.3%
ahmedabad 47997
 
1.9%
pune 47642
 
1.8%
london 45760
 
1.8%
Other values (216) 980238
37.8%
2024-06-10T16:49:58.282726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1633811
 
10.0%
n 1550488
 
9.5%
a 1470093
 
9.0%
l 1390524
 
8.5%
u 1215804
 
7.5%
b 1110841
 
6.8%
o 969196
 
5.9%
e 964509
 
5.9%
D 951019
 
5.8%
r 602224
 
3.7%
Other values (54) 4460904
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16319413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1633811
 
10.0%
n 1550488
 
9.5%
a 1470093
 
9.0%
l 1390524
 
8.5%
u 1215804
 
7.5%
b 1110841
 
6.8%
o 969196
 
5.9%
e 964509
 
5.9%
D 951019
 
5.8%
r 602224
 
3.7%
Other values (54) 4460904
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16319413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1633811
 
10.0%
n 1550488
 
9.5%
a 1470093
 
9.0%
l 1390524
 
8.5%
u 1215804
 
7.5%
b 1110841
 
6.8%
o 969196
 
5.9%
e 964509
 
5.9%
D 951019
 
5.8%
r 602224
 
3.7%
Other values (54) 4460904
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16319413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1633811
 
10.0%
n 1550488
 
9.5%
a 1470093
 
9.0%
l 1390524
 
8.5%
u 1215804
 
7.5%
b 1110841
 
6.8%
o 969196
 
5.9%
e 964509
 
5.9%
D 951019
 
5.8%
r 602224
 
3.7%
Other values (54) 4460904
27.3%

region
Text

Distinct85
Distinct (%)< 0.1%
Missing46
Missing (%)< 0.1%
Memory size34.3 MiB
2024-06-10T16:49:58.780849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.650290248
Min length4

Characters and Unicode

Total characters10455029
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN-WB
2nd rowIN-WB
3rd rowIN-WB
4th rowIN-WB
5th rowIN-WB
ValueCountFrequency (%)
ie-d 839768
37.4%
in-mm 194570
 
8.7%
in-ka 149219
 
6.6%
br-sp 129614
 
5.8%
in-dl 79922
 
3.6%
gb-eng 60519
 
2.7%
in-ga 58495
 
2.6%
br-mg 54426
 
2.4%
br-pe 49570
 
2.2%
in-gj 47997
 
2.1%
Other values (75) 584153
26.0%
2024-06-10T16:49:59.530889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2248321
21.5%
I 1598923
15.3%
E 996561
9.5%
D 934659
8.9%
N 852541
 
8.2%
B 559380
 
5.4%
R 522100
 
5.0%
M 514356
 
4.9%
A 366351
 
3.5%
S 349859
 
3.3%
Other values (23) 1511978
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10455029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2248321
21.5%
I 1598923
15.3%
E 996561
9.5%
D 934659
8.9%
N 852541
 
8.2%
B 559380
 
5.4%
R 522100
 
5.0%
M 514356
 
4.9%
A 366351
 
3.5%
S 349859
 
3.3%
Other values (23) 1511978
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10455029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2248321
21.5%
I 1598923
15.3%
E 996561
9.5%
D 934659
8.9%
N 852541
 
8.2%
B 559380
 
5.4%
R 522100
 
5.0%
M 514356
 
4.9%
A 366351
 
3.5%
S 349859
 
3.3%
Other values (23) 1511978
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10455029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2248321
21.5%
I 1598923
15.3%
E 996561
9.5%
D 934659
8.9%
N 852541
 
8.2%
B 559380
 
5.4%
R 522100
 
5.0%
M 514356
 
4.9%
A 366351
 
3.5%
S 349859
 
3.3%
Other values (23) 1511978
14.5%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.3 MiB
2024-06-10T16:49:59.830946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length20
Median length14
Mean length6.781707415
Min length5

Characters and Unicode

Total characters15247306
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia
ValueCountFrequency (%)
ireland 862584
35.0%
india 708558
28.7%
brazil 425182
17.2%
united 218426
 
8.9%
states 157905
 
6.4%
kingdom 60519
 
2.5%
canada 9483
 
0.4%
portugal 4728
 
0.2%
france 4155
 
0.2%
barbados 3766
 
0.2%
Other values (13) 11423
 
0.5%
2024-06-10T16:50:00.430917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2211655
14.5%
n 1870206
12.3%
d 1863336
12.2%
I 1574312
10.3%
i 1415957
9.3%
r 1305522
8.6%
l 1295664
8.5%
e 1246381
8.2%
t 543245
 
3.6%
B 428948
 
2.8%
Other values (24) 1492080
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15247306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2211655
14.5%
n 1870206
12.3%
d 1863336
12.2%
I 1574312
10.3%
i 1415957
9.3%
r 1305522
8.6%
l 1295664
8.5%
e 1246381
8.2%
t 543245
 
3.6%
B 428948
 
2.8%
Other values (24) 1492080
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15247306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2211655
14.5%
n 1870206
12.3%
d 1863336
12.2%
I 1574312
10.3%
i 1415957
9.3%
r 1305522
8.6%
l 1295664
8.5%
e 1246381
8.2%
t 543245
 
3.6%
B 428948
 
2.8%
Other values (24) 1492080
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15247306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2211655
14.5%
n 1870206
12.3%
d 1863336
12.2%
I 1574312
10.3%
i 1415957
9.3%
r 1305522
8.6%
l 1295664
8.5%
e 1246381
8.2%
t 543245
 
3.6%
B 428948
 
2.8%
Other values (24) 1492080
9.8%

continent
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing171222
Missing (%)7.6%
Memory size34.3 MiB
2024-06-10T16:50:00.664082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4154154
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAS
2nd rowAS
3rd rowAS
4th rowAS
5th rowAS
ValueCountFrequency (%)
eu 941483
45.3%
as 709593
34.2%
sa 425955
20.5%
af 46
 
< 0.1%
2024-06-10T16:50:01.305614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1135594
27.3%
S 1135548
27.3%
E 941483
22.7%
U 941483
22.7%
F 46
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4154154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1135594
27.3%
S 1135548
27.3%
E 941483
22.7%
U 941483
22.7%
F 46
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4154154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1135594
27.3%
S 1135548
27.3%
E 941483
22.7%
U 941483
22.7%
F 46
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4154154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1135594
27.3%
S 1135548
27.3%
E 941483
22.7%
U 941483
22.7%
F 46
 
< 0.1%